Quality-driven workforce performance evaluation based on robust regression and ANOMR/ANOMRV chart

Zhenrui Wang, Sean Dessureault and Jian Liu

IISE Transactions, 2013, vol. 45, issue 6, 644-657

Abstract: The integration of quality improvement and manufacturing system management has emerged as a promising research topic in recent years. Since operators’ performance variation can be reflected in product quality, workforce performance evaluation should be conducted with quality-based metrics to improve product quality as well as manufacturing system productivity. In this article, a methodology incorporating regression modeling and multiple comparisons is proposed to aid the performance evaluation. The effects of other impacting factors that contribute to operators’ performance variation are quantified with a robust zero-inflated Poisson regression model. The model coefficients are analyzed with multiple hypothesis tests to identify underperforming machines. Two statistical charts used in multiple comparisons are adopted for identifying underperforming operators. A case study with data from a real-world production system and a simulation experiment are presented to demonstrate the proposed methodology.

Date: 2013
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/0740817X.2012.733486 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:uiiexx:v:45:y:2013:i:6:p:644-657

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/uiie20

DOI: 10.1080/0740817X.2012.733486

Access Statistics for this article

IISE Transactions is currently edited by Jianjun Shi

More articles in IISE Transactions from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().